https://github.com/sosuperic/MeanSum
https://github.com/sosuperic/MeanSum
Last synced: 18 days ago
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- Host: GitHub
- URL: https://github.com/sosuperic/MeanSum
- Owner: sosuperic
- License: other
- Created: 2019-01-24T20:52:25.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-07T17:55:28.000Z (about 5 years ago)
- Last Synced: 2024-11-03T06:32:58.829Z (6 months ago)
- Language: Python
- Size: 355 KB
- Stars: 112
- Watchers: 9
- Forks: 51
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-Multi-Document-Summarization - sosuperic/MeanSum
README
# MeanSum: A Model for Unsupervised Neural Multi-Document Abstractive Summarization
Corresponding paper, accepted to ICML 2019: [https://arxiv.org/abs/1810.05739](https://arxiv.org/abs/1810.05739).
## Requirements
Main requirements:
- python 3
- torch 0.4.0
Rest of python packages in ```requirements.txt```.
Tested in Docker, image = ```pytorch/pytorch:0.4_cuda9_cudnn7```.
## General setup
Execute inside ```scripts/```:
##### Create directories that aren't part of the Git repo (checkpoints/, outputs/):
```
bash setup_dirs.sh
```
##### Install python packages:
```
bash install_python_pkgs.sh
```
##### The default parameters for Tensorboard(x?) cause texts from writer.add_text() to not show up. Update by:
```
python update_tensorboard.py
```
## Downloading data and pretrained models
### Data
1. Download Yelp data: https://www.yelp.com/dataset and place files in ```datasets/yelp_dataset/```
2. Run script to pre-process script and create train, val, test splits:
```
bash scripts/preprocess_data.sh
```
3. Download subword tokenizer built on Yelp and place in
```datasets/yelp_dataset/processed/```:
[link](https://s3.us-east-2.amazonaws.com/unsup-sum/subwordenc_32000_maxrevs260_fixed.pkl)
### Pre-trained models
1. Download summarization model and place in
```stable_checkpoints/sum/mlstm/yelp/batch_size_16-notes_cycloss_honly-sum_lr_0.0005-tau_2.0/```:
[link](https://s3.us-east-2.amazonaws.com/unsup-sum/sum_e0_tot3.32_r1f0.27.pt)
2. Download language model and place in
```stable_checkpoints/lm/mlstm/yelp/batch_size_512-lm_lr_0.001-notes_data260_fixed/```:
[link](https://s3.us-east-2.amazonaws.com/unsup-sum/lm_e24_2.88.pt)
3. Download classification model and place in
```stable_checkpoints/clf/cnn/yelp/batch_size_256-notes_data260_fixed/```:
[link](https://s3.us-east-2.amazonaws.com/unsup-sum/clf_e10_l0.6760_a0.7092.pt)
### Reference summaries
Download from: [link](https://s3.us-east-2.amazonaws.com/unsup-sum/summaries_0-200_cleaned.csv).
Each row contains "Input.business_id", "Input.original_review_\\_id",
"Input.original_review__\\_", "Answer.summary", etc. The "Answer.summary" is the
reference summary written by the Mechanical Turk worker.
## Running
Testing with pretrained mode. This will output and save the automated metrics.
Results will be in ```outputs/eval/yelp/n_docs_8/unsup_```
NOTE: Unlike some conventions, 'gpus' option here represents the GPU ID (the one which is visible) and NOT the number of GPUs. Hence, for a machine with a single GPU, you will give gpus=0
```
python train_sum.py --mode=test --gpus=0 --batch_size=16 --notes=
```
Training summarization model (using pre-trained language model and default hyperparams).
The automated metrics results will be in ```checkpoints/sum/mlstm/yelp/_```.:
```
python train_sum.py --batch_size=16 --gpus=0,1,2,3 --notes=
```